1. Field of the Invention
The present invention relates to communications signal transmission and detection, and in particular to adaptive signal equalization for compensation of signal distortions caused by signal dispersion and nonlinearities within signal transmission media.
2. Description of the Related Art
Signal processing architectures for mitigation of different kinds of channel impairments and/or timing recovery and synchronization functions as used for communications transmission and/or storage systems can be divided into two categories: (1) discrete-time architecture (this architecture uses a sampled approach to convert the input continuous-time, analog waveform into a discrete signal and is commonly used in current systems; typically, a high resolution analog-to-digital converter, which follows the analog anti-aliasing filter, is used as the sampler at the analog front end); and (2) continuous-time architecture (this architecture is an analog continuous-time approach which directly processes the incoming analog waveform for mitigating channel impairments or timing recovery functions while remaining in the continuous time domain until the final data bit stream is generated).
In continuous-time signal processing architectures, various system analog components have different frequency-dependent group delays which also vary with dependencies upon variations in fabrication processes, operating temperatures, etc. It becomes important for such architectures to construct an adaptive timing control block which can substantially compensate for (e.g., match) the unknown latency of certain analog components or group of analog components so as to minimize the bit error rate (BER) of the data signal transmission (or improve some other parameter indicative of the data symbol detection reliability). One such parameter, referred to as the Mean-Squared Error (MSE) and computed as the average (continuous-time or sampled) of the square of the difference between the input and the output signals to a decision device (e.g., a signal slicer), is particularly important to this application. It has become known that adapting the tap coefficients in a certain manner so as to minimize the MSE tends to reduce the BER as well.
Fractional-spaced feedforward filters have commonly been used either as stand-alone linear equalizers or in combination with decision feedback. The adaptation technique for the tap coefficients implicitly assume independence in the adaptation of the successive tap coefficients, which has been based on minimizing the mean squared error (as computed as the difference between the slicer input, or pre-slice, signal and slicer output, or post-slice, signal). This adaptation technique is referred to as least mean square error (LMS error or LMSE) or minimum mean square error (MMSE) adaptation. It can be shown that the LMSE adaptation for both fractional feedforward or symbol spaced feedback at iteration k+1 reduces to the following coefficient update equation:
      c    _    =            ∫      0      t        ⁢                  μ        ·                  e          ⁡                      (            t            )                              ⁢                        s          _                ⁡                  (          t          )                    ⁢                          ⁢              ⅆ        t            ⁢                          ⁢              (                  continuous          ⁢                      -                    ⁢          time          ⁢                                          ⁢          adaptation          ⁢                                          ⁢          case                )            where c is the tap coefficient vector and e(t) the corresponding error (between delay-aligned slicer input and output), s is the vector with components as the input waveform to the corresponding tap mixer and μ is a constant and is an adaptation parameter.
Referring to FIG. 1, a conventional adaptive signal equalizer 10 includes a feedforward filter 12, an adaptive coefficients generator 14 and a data symbol decision circuit (e.g., signal slicer) 16. Additionally, if decision feedback equalization is desired, a feedback filter 20 further filters the final output signal 17 from the decision circuit 16 to provide a feedback signal 21 which is combined in a signal combiner 22 (e.g., signal summing circuit) with the initially equalized signal 13 provided by the feedforward filter 12. The resulting equalized signal 13/23 is tested (e.g., sliced) by the decision circuit 16 to produce the output signal 17.
An additional signal combining circuit 18 combines the input 13/23 and output 17 signals of the decision circuit 16 to provide the error signal 19 representing the difference between the pre-decision 13/23 and post-decision 17 signals. As is well known, this error signal 19 is processed by the adaptive coefficients generator 14, along with the incoming data signal 11, to produce the adaptive coefficients 15 for the feedforward filter 12.
Additionally, so as to compensate for internal signal delays ts, te within the feedforward filter 12 and decision circuit 16, signal delay circuits 24s, 24e can be included in the signal paths for the incoming data signal 11 and pre-decision signal 13/23. Accordingly, the signal 25e to the signal combining circuit 18 is a delayed form of the pre-decision signal 13/23.
Referring to FIG. 2, a conventional feedforward filter 12 processes the incoming data signal 11 to produce the equalized signal 13 using a series of signal delay elements 32, multiplier circuits 34 and output summing circuit 36 in accordance with well-known techniques. Each of the successively delayed versions 33a, 33b, . . . , 33n, as well as the incoming data signal 11, is multiplied in one of the multiplication circuits 34a, 34b, 34c, 34n with its respective adaptive coefficient signal 15a, 15b, . . . , 15n. The resulting product signals 35a, 35b, . . . , 35n are summed in the signal summing circuit 36, with the resulting sum signal forming the equalized signal 13.
Referring to FIG. 3, a conventional adaptive coefficients generator 14 processes the incoming data signal 11 and feedback error signal 19 using a series of signal delay elements 42, signal multipliers 44 and signal integrators (e.g., low pass filters) 46 in accordance with well known techniques. The incoming signal 11 is successively delayed by the signal delay elements 42a, 42b, . . . , 42n to produce successively delayed versions 43a, 43b, . . . , 43n of the incoming signal 11. Each of these signals 11, 43a, 43b, . . . , 43n is multiplied in its respective signal multiplier 44a, 44b, . . . , 44n with the feedback error signal 19. The resulting product signals 45a, 45b, . . . , 45n are individually integrated in the signal integration circuits 46a, 46b, . . . , 46n to produce the individual adaptive coefficient signals 15a, 15b, . . . , 15n. 
Referring to FIG. 4, one conventional technique for obtaining the appropriate sampling phase for a continuous-time signal that is being converted to a discrete signal involves the use of a clock and data recovery (CDR) circuit 50. The incoming signal 51 is sampled by a signal sampler 52 which is clocked by a clock signal 59 to recover the embedded data 53. The clock signal 59 is the output of an oscillator 58 (e.g., voltage-controlled oscillator) and is compared in signal phase with the incoming signal 51 in a phase detector 54. The phase detection signal 55 is filtered by the loop filter 56 (e.g., a low pass filter), with the filtered signal 57 controlling the oscillator 58.
While this circuitry 50 has proven to be useful in many applications, it is nonetheless insufficiently adaptive for compensating for the above-noted variable characteristics of analog circuitry and components.